Conference Paper

Ontology of Folksonomy: A New Modeling Method

Conference: Proceedings of the Semantic Authoring, Annotation and Knowledge Markup Workshop (SAAKM2007) located at the 4th International Conference on Knowledge Capture (KCap 2007), Whistler, British Columbia, Canada, October 28-31, 2007
Source: DBLP


ABSTRACT Ontologies and ,tagging ,systems ,are two ,different ways ,to organize,the knowledge ,present in Web. ,The first one ,has a formal,fundamental ,that derives ,from ,descriptive logic and artificial intelligence. The other one is simpler ,and it integrates heterogeneous contents, and it is based on the collaboration of users in the Web 2.0. In this paper we propose a method to model tagging,systems ,like folksonomies ,using ontologies. In our proposal, structured information (ontologies) can be extracted from,knowledge ,built in a ,simple ,and ,collaborative ,way (folksonomies). Furthermore, we provide an analytical expression to evaluate the system requirements to store the derived ontology. Categories and Subject Descriptors H.1.1 [Models and Principles]: Systems and Information Theory – Information theory. General Terms

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Available from: Francisco Echarte
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    • "Resources can be any object that users are interested in tag, such as photos and videos. In comparison with ontologies, Folksonomies are simpler structures to be implemented and used [7]. According to [11], one of the benefits of the tagging process is that users do not need experience or skills to participate, i.e. the Folksonomies that emerge do not need to be built by knowledge engineers. "
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    ABSTRACT: A dialogue system allows a human to interact with a computer, through the natural language. One of the main components of a dialogue system is the Conceptual Model. The Conceptual Model represents a domain and its specification is given by several forms of knowledge representation. We propose to represent it using folksonomies. We describe a method called FolksDialogue that performs the learning of folksonomies from task-oriented dialogues. In order to check whether the structures created by the method are genuine folksonomies, we performed an experiment to prove that they have the small-world phenomenon, which is a characteristic of folksonomies. The generated folksonomies can be useful in the interpretation of dialogue utterances, indicating whether the utterances belong or not to the domains that the folksonomies represent. The experiments show that the folksonomies learned can perform the interpretation of utterances with an accuracy of 69.20%.
    Full-text · Conference Paper · Apr 2015
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    • "Gruber [8] discusses the differences of ontology and folksonomy and points out some design considerations for constructing ontologies from tags. The authors in [9] provide more details of the ontology model and an algorithm to create such ontology from the folksonomy. That algorithm basically covers the associations between user, resources and tags but does not include how to find tag types or properties. "
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    ABSTRACT: Ontologies are an important part of the Semantic Web as well as of many intelligent systems. However, the traditional expert-driven development of ontologies is time-consuming and often results in incomplete and inappropriate ontologies. In addition, since ontology evolution is not controlled by end users, it may take too long for a conceptual change in the domain to be reflected in the ontology. In this paper, we present a recommendation algorithm in a Web 2.0 platform that supports end users to collaboratively evolve ontologies by suggesting semantic relations between new and existing concepts. We use the Wikipedia category hierarchy to evaluate our algorithm and our experimental results show that the proposed algorithm produces high quality recommendations.
    Full-text · Conference Paper · Jul 2010
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    • "Wu et al. (2006a) used hierarchical clustering to build ontology from tags that also use similar-to relations. Later, ontology schemes that fits social tagging system were proposed, such as (Van Damme et al., 2007) and (Echarte et al., 2007), which mainly focused on the relation between tags, objects and users, rather than between tags themselves. Alexandre Passant (2007) mapped tags to domain ontologies manually to improve information retrieval in social media. "
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    ABSTRACT: Thanks to its simplicity, social tagging system has accumulated huge amount of user contributed tags. However, user contributed tags lack explicit hierarchical structure, while many tag-based applications would benefit if such a structure presents. In this work, we explore the structure of tags with a directed and easy-to-evaluate relation, named as the subsumption relation. We propose three methods to discover the subsumption relation between tags. Specifically, the tagged document's content is used to find the relations, which leads to better result. Besides relation discovery, we also propose a greedy algorithm to eliminate the redundant relations by constructing a Layered Directed Acyclic Graph (Layered-DAG) of tags. We perform quantitative evaluations on two real world data sets. The results show that our methods outperform hierarchical clustering-based approach. Empirical study of the constructed Layered-DAG and error analysis are also provided.
    Full-text · Conference Paper · Jan 2010
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